Overview

Dataset statistics

Number of variables14
Number of observations12753
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.5 MiB
Average record size in memory120.0 B

Variable types

Text2
Numeric12

Alerts

percentage0to15years is highly overall correlated with percentagehouseholdswithchildrenHigh correlation
percentage25to45years is highly overall correlated with percentage45to65years and 4 other fieldsHigh correlation
percentage45to65years is highly overall correlated with percentage25to45years and 2 other fieldsHigh correlation
percentage65yearsorolder is highly overall correlated with percentage25to45yearsHigh correlation
percentagehouseholdswithchildren is highly overall correlated with percentage0to15years and 1 other fieldsHigh correlation
percentagehouseholdswithoutchildren is highly overall correlated with percentage25to45years and 2 other fieldsHigh correlation
percentagenonwesternmigrationbackground is highly overall correlated with percentage25to45years and 5 other fieldsHigh correlation
percentageonepersonhouseholds is highly overall correlated with percentagehouseholdswithchildren and 3 other fieldsHigh correlation
percentagewesternmigrationbackground is highly overall correlated with percentagenonwesternmigrationbackground and 1 other fieldsHigh correlation
populationdensityperkm2 is highly overall correlated with percentage25to45years and 2 other fieldsHigh correlation
neighborhoodcode has unique valuesUnique
percentagenonwesternmigrationbackground has 1167 (9.2%) zerosZeros

Reproduction

Analysis started2024-07-05 09:51:21.659105
Analysis finished2024-07-05 09:51:46.057667
Duration24.4 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

neighborhoodcode
Text

UNIQUE 

Distinct12753
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size199.3 KiB
2024-07-05T11:51:46.330036image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters127530
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12753 ?
Unique (%)100.0%

Sample

1st rowBU00140000
2nd rowBU06640551
3rd rowBU18960310
4th rowBU03922001
5th rowBU18760403
ValueCountFrequency (%)
bu00140000 1
 
< 0.1%
bu19402901 1
 
< 0.1%
bu05050509 1
 
< 0.1%
bu18960310 1
 
< 0.1%
bu03922001 1
 
< 0.1%
bu18760403 1
 
< 0.1%
bu08551704 1
 
< 0.1%
bu02431207 1
 
< 0.1%
bu01601903 1
 
< 0.1%
bu04792130 1
 
< 0.1%
Other values (12743) 12743
99.9%
2024-07-05T11:51:46.752416image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 35888
28.1%
1 15120
11.9%
B 12753
 
10.0%
U 12753
 
10.0%
2 8050
 
6.3%
3 7850
 
6.2%
9 6913
 
5.4%
4 6274
 
4.9%
5 6009
 
4.7%
7 5363
 
4.2%
Other values (2) 10557
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 102024
80.0%
Uppercase Letter 25506
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 35888
35.2%
1 15120
14.8%
2 8050
 
7.9%
3 7850
 
7.7%
9 6913
 
6.8%
4 6274
 
6.1%
5 6009
 
5.9%
7 5363
 
5.3%
6 5344
 
5.2%
8 5213
 
5.1%
Uppercase Letter
ValueCountFrequency (%)
B 12753
50.0%
U 12753
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 102024
80.0%
Latin 25506
 
20.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 35888
35.2%
1 15120
14.8%
2 8050
 
7.9%
3 7850
 
7.7%
9 6913
 
6.8%
4 6274
 
6.1%
5 6009
 
5.9%
7 5363
 
5.3%
6 5344
 
5.2%
8 5213
 
5.1%
Latin
ValueCountFrequency (%)
B 12753
50.0%
U 12753
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 127530
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 35888
28.1%
1 15120
11.9%
B 12753
 
10.0%
U 12753
 
10.0%
2 8050
 
6.3%
3 7850
 
6.2%
9 6913
 
5.4%
4 6274
 
4.9%
5 6009
 
4.7%
7 5363
 
4.2%
Other values (2) 10557
 
8.3%
Distinct11857
Distinct (%)93.0%
Missing0
Missing (%)0.0%
Memory size199.3 KiB
2024-07-05T11:51:47.058303image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length60
Median length49
Mean length15.753548
Min length2

Characters and Unicode

Total characters200905
Distinct characters81
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11419 ?
Unique (%)89.5%

Sample

1st rowBinnenstad-Noord
2nd rowDe Schenge
3rd rowStreukel-Holten-Genne
4th rowZuid-Schalkwijkerweg
5th rowDrempt
ValueCountFrequency (%)
verspreide 1156
 
5.0%
huizen 1154
 
5.0%
de 807
 
3.5%
en 613
 
2.6%
buitengebied 611
 
2.6%
noord 312
 
1.3%
zuid 281
 
1.2%
kern 251
 
1.1%
243
 
1.0%
omgeving 239
 
1.0%
Other values (9780) 17551
75.6%
2024-07-05T11:51:47.707088image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 31705
15.8%
r 15474
 
7.7%
n 13716
 
6.8%
i 12088
 
6.0%
o 10476
 
5.2%
10468
 
5.2%
t 9553
 
4.8%
d 9349
 
4.7%
u 8959
 
4.5%
a 8017
 
4.0%
Other values (71) 71100
35.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 165146
82.2%
Uppercase Letter 21451
 
10.7%
Space Separator 10468
 
5.2%
Dash Punctuation 2490
 
1.2%
Other Punctuation 684
 
0.3%
Decimal Number 485
 
0.2%
Open Punctuation 89
 
< 0.1%
Close Punctuation 89
 
< 0.1%
Math Symbol 3
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 31705
19.2%
r 15474
9.4%
n 13716
 
8.3%
i 12088
 
7.3%
o 10476
 
6.3%
t 9553
 
5.8%
d 9349
 
5.7%
u 8959
 
5.4%
a 8017
 
4.9%
s 7952
 
4.8%
Other values (25) 37857
22.9%
Uppercase Letter
ValueCountFrequency (%)
B 2499
 
11.6%
V 2029
 
9.5%
D 1506
 
7.0%
H 1477
 
6.9%
W 1438
 
6.7%
O 1328
 
6.2%
N 1293
 
6.0%
S 1267
 
5.9%
Z 1190
 
5.5%
K 1079
 
5.0%
Other values (15) 6345
29.6%
Decimal Number
ValueCountFrequency (%)
1 113
23.3%
2 97
20.0%
0 97
20.0%
3 88
18.1%
4 35
 
7.2%
5 22
 
4.5%
6 10
 
2.1%
7 8
 
1.6%
8 8
 
1.6%
9 7
 
1.4%
Other Punctuation
ValueCountFrequency (%)
. 344
50.3%
' 132
 
19.3%
, 109
 
15.9%
/ 93
 
13.6%
& 4
 
0.6%
" 2
 
0.3%
Space Separator
ValueCountFrequency (%)
10468
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2490
100.0%
Open Punctuation
ValueCountFrequency (%)
( 89
100.0%
Close Punctuation
ValueCountFrequency (%)
) 89
100.0%
Math Symbol
ValueCountFrequency (%)
+ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 186597
92.9%
Common 14308
 
7.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 31705
17.0%
r 15474
 
8.3%
n 13716
 
7.4%
i 12088
 
6.5%
o 10476
 
5.6%
t 9553
 
5.1%
d 9349
 
5.0%
u 8959
 
4.8%
a 8017
 
4.3%
s 7952
 
4.3%
Other values (50) 59308
31.8%
Common
ValueCountFrequency (%)
10468
73.2%
- 2490
 
17.4%
. 344
 
2.4%
' 132
 
0.9%
1 113
 
0.8%
, 109
 
0.8%
2 97
 
0.7%
0 97
 
0.7%
/ 93
 
0.6%
( 89
 
0.6%
Other values (11) 276
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 200832
> 99.9%
None 73
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 31705
15.8%
r 15474
 
7.7%
n 13716
 
6.8%
i 12088
 
6.0%
o 10476
 
5.2%
10468
 
5.2%
t 9553
 
4.8%
d 9349
 
4.7%
u 8959
 
4.5%
a 8017
 
4.0%
Other values (62) 71027
35.4%
None
ValueCountFrequency (%)
ë 30
41.1%
â 20
27.4%
é 7
 
9.6%
û 6
 
8.2%
ö 4
 
5.5%
ï 3
 
4.1%
ô 1
 
1.4%
ú 1
 
1.4%
á 1
 
1.4%

populationdensityperkm2
Real number (ℝ)

HIGH CORRELATION 

Distinct6242
Distinct (%)48.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3403.9544
Minimum1
Maximum52015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size199.3 KiB
2024-07-05T11:51:47.907037image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile20
Q1198
median2302
Q35279
95-th percentile10178.6
Maximum52015
Range52014
Interquartile range (IQR)5081

Descriptive statistics

Standard deviation3977.1647
Coefficient of variation (CV)1.1683954
Kurtosis9.3216224
Mean3403.9544
Median Absolute Deviation (MAD)2219
Skewness2.2871554
Sum43410631
Variance15817839
MonotonicityNot monotonic
2024-07-05T11:51:48.107275image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 58
 
0.5%
15 58
 
0.5%
37 53
 
0.4%
16 52
 
0.4%
43 52
 
0.4%
23 51
 
0.4%
19 49
 
0.4%
11 49
 
0.4%
14 49
 
0.4%
32 47
 
0.4%
Other values (6232) 12235
95.9%
ValueCountFrequency (%)
1 1
 
< 0.1%
2 2
 
< 0.1%
3 3
 
< 0.1%
4 9
 
0.1%
5 19
0.1%
6 11
 
0.1%
7 28
0.2%
8 32
0.3%
9 46
0.4%
10 39
0.3%
ValueCountFrequency (%)
52015 1
< 0.1%
36812 1
< 0.1%
35363 1
< 0.1%
34026 1
< 0.1%
31642 1
< 0.1%
31431 1
< 0.1%
31185 1
< 0.1%
30763 1
< 0.1%
30540 1
< 0.1%
29494 1
< 0.1%

percentage0to15years
Real number (ℝ)

HIGH CORRELATION 

Distinct43
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.685251
Minimum0
Maximum48
Zeros54
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size199.3 KiB
2024-07-05T11:51:48.306904image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q112
median15
Q317
95-th percentile23
Maximum48
Range48
Interquartile range (IQR)5

Descriptive statistics

Standard deviation5.1416159
Coefficient of variation (CV)0.35012109
Kurtosis1.7474532
Mean14.685251
Median Absolute Deviation (MAD)3
Skewness0.38091495
Sum187281
Variance26.436214
MonotonicityNot monotonic
2024-07-05T11:51:48.490203image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
15 1292
 
10.1%
14 1259
 
9.9%
13 1177
 
9.2%
16 1176
 
9.2%
17 1066
 
8.4%
12 877
 
6.9%
18 769
 
6.0%
11 719
 
5.6%
19 564
 
4.4%
10 523
 
4.1%
Other values (33) 3331
26.1%
ValueCountFrequency (%)
0 54
 
0.4%
1 43
 
0.3%
2 64
 
0.5%
3 88
 
0.7%
4 120
 
0.9%
5 164
1.3%
6 184
1.4%
7 232
1.8%
8 304
2.4%
9 403
3.2%
ValueCountFrequency (%)
48 1
 
< 0.1%
41 1
 
< 0.1%
40 2
 
< 0.1%
39 1
 
< 0.1%
38 1
 
< 0.1%
37 5
 
< 0.1%
36 4
 
< 0.1%
35 11
0.1%
34 19
0.1%
33 19
0.1%

percentage15to25years
Real number (ℝ)

Distinct76
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.330746
Minimum0
Maximum99
Zeros14
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size199.3 KiB
2024-07-05T11:51:48.673331image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q110
median12
Q314
95-th percentile19
Maximum99
Range99
Interquartile range (IQR)4

Descriptive statistics

Standard deviation5.2551799
Coefficient of variation (CV)0.42618509
Kurtosis49.057337
Mean12.330746
Median Absolute Deviation (MAD)2
Skewness4.9945154
Sum157254
Variance27.616916
MonotonicityNot monotonic
2024-07-05T11:51:48.891018image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11 1884
14.8%
12 1694
13.3%
10 1609
12.6%
13 1306
10.2%
9 1142
9.0%
14 1006
7.9%
15 725
 
5.7%
8 645
 
5.1%
16 487
 
3.8%
7 417
 
3.3%
Other values (66) 1838
14.4%
ValueCountFrequency (%)
0 14
 
0.1%
1 14
 
0.1%
2 29
 
0.2%
3 37
 
0.3%
4 71
 
0.6%
5 126
 
1.0%
6 218
 
1.7%
7 417
 
3.3%
8 645
5.1%
9 1142
9.0%
ValueCountFrequency (%)
99 1
< 0.1%
95 1
< 0.1%
83 1
< 0.1%
81 1
< 0.1%
80 1
< 0.1%
79 2
< 0.1%
75 2
< 0.1%
73 1
< 0.1%
72 2
< 0.1%
71 1
< 0.1%

percentage25to45years
Real number (ℝ)

HIGH CORRELATION 

Distinct76
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.475339
Minimum0
Maximum80
Zeros6
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size199.3 KiB
2024-07-05T11:51:49.106593image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12
Q118
median21
Q326
95-th percentile38
Maximum80
Range80
Interquartile range (IQR)8

Descriptive statistics

Standard deviation8.1036286
Coefficient of variation (CV)0.36055646
Kurtosis4.2739834
Mean22.475339
Median Absolute Deviation (MAD)4
Skewness1.424763
Sum286628
Variance65.668797
MonotonicityNot monotonic
2024-07-05T11:51:49.307094image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 1010
 
7.9%
19 882
 
6.9%
21 881
 
6.9%
22 870
 
6.8%
23 787
 
6.2%
18 745
 
5.8%
24 701
 
5.5%
17 692
 
5.4%
25 540
 
4.2%
16 534
 
4.2%
Other values (66) 5111
40.1%
ValueCountFrequency (%)
0 6
 
< 0.1%
1 5
 
< 0.1%
2 5
 
< 0.1%
3 9
 
0.1%
4 18
 
0.1%
5 12
 
0.1%
6 32
0.3%
7 39
0.3%
8 64
0.5%
9 69
0.5%
ValueCountFrequency (%)
80 2
 
< 0.1%
76 2
 
< 0.1%
74 2
 
< 0.1%
73 1
 
< 0.1%
71 1
 
< 0.1%
70 1
 
< 0.1%
69 5
< 0.1%
68 3
< 0.1%
67 1
 
< 0.1%
66 1
 
< 0.1%

percentage45to65years
Real number (ℝ)

HIGH CORRELATION 

Distinct57
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.374579
Minimum0
Maximum57
Zeros13
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size199.3 KiB
2024-07-05T11:51:49.506640image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile19
Q126
median29
Q333
95-th percentile39
Maximum57
Range57
Interquartile range (IQR)7

Descriptive statistics

Standard deviation6.4279476
Coefficient of variation (CV)0.21882689
Kurtosis1.6968739
Mean29.374579
Median Absolute Deviation (MAD)4
Skewness-0.33193807
Sum374614
Variance41.31851
MonotonicityNot monotonic
2024-07-05T11:51:49.690123image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27 898
 
7.0%
28 896
 
7.0%
29 881
 
6.9%
30 879
 
6.9%
31 814
 
6.4%
26 749
 
5.9%
33 704
 
5.5%
25 691
 
5.4%
32 685
 
5.4%
34 592
 
4.6%
Other values (47) 4964
38.9%
ValueCountFrequency (%)
0 13
0.1%
1 12
0.1%
2 9
0.1%
3 12
0.1%
4 7
0.1%
5 4
 
< 0.1%
6 10
0.1%
7 5
 
< 0.1%
8 7
0.1%
9 17
0.1%
ValueCountFrequency (%)
57 1
 
< 0.1%
56 1
 
< 0.1%
55 2
 
< 0.1%
53 5
 
< 0.1%
52 5
 
< 0.1%
51 5
 
< 0.1%
50 7
 
0.1%
49 13
0.1%
48 6
 
< 0.1%
47 32
0.3%

percentage65yearsorolder
Real number (ℝ)

HIGH CORRELATION 

Distinct87
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.13832
Minimum0
Maximum100
Zeros37
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size199.3 KiB
2024-07-05T11:51:49.906650image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8
Q116
median20
Q325
95-th percentile37
Maximum100
Range100
Interquartile range (IQR)9

Descriptive statistics

Standard deviation9.3976859
Coefficient of variation (CV)0.44458054
Kurtosis6.1288548
Mean21.13832
Median Absolute Deviation (MAD)5
Skewness1.3852045
Sum269577
Variance88.3165
MonotonicityNot monotonic
2024-07-05T11:51:50.122787image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 727
 
5.7%
22 726
 
5.7%
19 709
 
5.6%
21 685
 
5.4%
18 676
 
5.3%
23 631
 
4.9%
16 577
 
4.5%
17 573
 
4.5%
24 564
 
4.4%
25 530
 
4.2%
Other values (77) 6355
49.8%
ValueCountFrequency (%)
0 37
 
0.3%
1 44
 
0.3%
2 57
 
0.4%
3 66
 
0.5%
4 77
0.6%
5 92
0.7%
6 110
0.9%
7 145
1.1%
8 179
1.4%
9 183
1.4%
ValueCountFrequency (%)
100 1
< 0.1%
98 1
< 0.1%
97 2
< 0.1%
96 1
< 0.1%
90 2
< 0.1%
89 1
< 0.1%
88 1
< 0.1%
87 2
< 0.1%
85 1
< 0.1%
84 2
< 0.1%

percentageonepersonhouseholds
Real number (ℝ)

HIGH CORRELATION 

Distinct122
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.651737
Minimum0
Maximum100
Zeros10
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size199.3 KiB
2024-07-05T11:51:50.347730image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14
Q123
median30
Q340
95-th percentile60
Maximum100
Range100
Interquartile range (IQR)17

Descriptive statistics

Standard deviation14.540175
Coefficient of variation (CV)0.44531092
Kurtosis1.3934609
Mean32.651737
Median Absolute Deviation (MAD)8
Skewness1.0331189
Sum416407.6
Variance211.41669
MonotonicityNot monotonic
2024-07-05T11:51:50.540779image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25 458
 
3.6%
24 452
 
3.5%
26 447
 
3.5%
29 445
 
3.5%
27 438
 
3.4%
30 437
 
3.4%
28 429
 
3.4%
23 417
 
3.3%
31 411
 
3.2%
21 407
 
3.2%
Other values (112) 8412
66.0%
ValueCountFrequency (%)
0 10
 
0.1%
1 3
 
< 0.1%
3 4
 
< 0.1%
4 10
 
0.1%
5 25
0.2%
6 22
 
0.2%
7 25
0.2%
8 35
0.3%
9 41
0.3%
10 62
0.5%
ValueCountFrequency (%)
100 4
< 0.1%
99 4
< 0.1%
98 1
 
< 0.1%
97 1
 
< 0.1%
96 3
< 0.1%
95 6
< 0.1%
94 4
< 0.1%
93 7
0.1%
92 2
 
< 0.1%
91 3
< 0.1%

percentagehouseholdswithoutchildren
Real number (ℝ)

HIGH CORRELATION 

Distinct95
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.296824
Minimum0
Maximum73
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size199.3 KiB
2024-07-05T11:51:50.722923image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile19
Q127
median32
Q337
95-th percentile46
Maximum73
Range73
Interquartile range (IQR)10

Descriptive statistics

Standard deviation8.3622664
Coefficient of variation (CV)0.25891915
Kurtosis0.75051204
Mean32.296824
Median Absolute Deviation (MAD)5
Skewness0.12169591
Sum411881.4
Variance69.9275
MonotonicityNot monotonic
2024-07-05T11:51:50.922789image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33 675
 
5.3%
34 670
 
5.3%
32 664
 
5.2%
35 660
 
5.2%
31 635
 
5.0%
36 611
 
4.8%
37 566
 
4.4%
29 553
 
4.3%
28 532
 
4.2%
30 532
 
4.2%
Other values (85) 6655
52.2%
ValueCountFrequency (%)
0 5
< 0.1%
1 5
< 0.1%
2 6
< 0.1%
3 5
< 0.1%
4 4
 
< 0.1%
5 11
0.1%
6 7
0.1%
7 8
0.1%
8 8
0.1%
9 10
0.1%
ValueCountFrequency (%)
73 1
 
< 0.1%
72 1
 
< 0.1%
71 1
 
< 0.1%
70 1
 
< 0.1%
67 2
 
< 0.1%
65 2
 
< 0.1%
64 2
 
< 0.1%
63 3
< 0.1%
62 6
< 0.1%
61 3
< 0.1%

percentagehouseholdswithchildren
Real number (ℝ)

HIGH CORRELATION 

Distinct103
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.05423
Minimum0
Maximum85
Zeros26
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size199.3 KiB
2024-07-05T11:51:51.105917image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14
Q129
median35
Q342
95-th percentile54
Maximum85
Range85
Interquartile range (IQR)13

Descriptive statistics

Standard deviation11.823991
Coefficient of variation (CV)0.33730569
Kurtosis0.67059549
Mean35.05423
Median Absolute Deviation (MAD)7
Skewness-0.071803419
Sum447046.6
Variance139.80677
MonotonicityNot monotonic
2024-07-05T11:51:51.337041image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36 563
 
4.4%
35 559
 
4.4%
38 557
 
4.4%
33 514
 
4.0%
37 509
 
4.0%
34 497
 
3.9%
39 474
 
3.7%
32 468
 
3.7%
40 461
 
3.6%
31 435
 
3.4%
Other values (93) 7716
60.5%
ValueCountFrequency (%)
0 26
0.2%
1 25
0.2%
2 16
 
0.1%
3 31
0.2%
4 15
 
0.1%
5 32
0.3%
6 52
0.4%
7 50
0.4%
8 46
0.4%
9 61
0.5%
ValueCountFrequency (%)
85 1
 
< 0.1%
82 1
 
< 0.1%
81 2
 
< 0.1%
80 1
 
< 0.1%
79 1
 
< 0.1%
78 1
 
< 0.1%
77 2
 
< 0.1%
76 2
 
< 0.1%
75 7
0.1%
74 2
 
< 0.1%

percentagewesternmigrationbackground
Real number (ℝ)

HIGH CORRELATION 

Distinct70
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.3657963
Minimum0
Maximum88
Zeros104
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size199.3 KiB
2024-07-05T11:51:51.522779image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q15
median8
Q312
95-th percentile21
Maximum88
Range88
Interquartile range (IQR)7

Descriptive statistics

Standard deviation6.7540043
Coefficient of variation (CV)0.72113509
Kurtosis14.198729
Mean9.3657963
Median Absolute Deviation (MAD)3
Skewness2.671837
Sum119442
Variance45.616575
MonotonicityNot monotonic
2024-07-05T11:51:51.705575image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 1133
 
8.9%
7 1083
 
8.5%
8 1082
 
8.5%
5 1009
 
7.9%
9 994
 
7.8%
4 987
 
7.7%
10 850
 
6.7%
3 774
 
6.1%
11 745
 
5.8%
12 568
 
4.5%
Other values (60) 3528
27.7%
ValueCountFrequency (%)
0 104
 
0.8%
1 245
 
1.9%
2 479
3.8%
3 774
6.1%
4 987
7.7%
5 1009
7.9%
6 1133
8.9%
7 1083
8.5%
8 1082
8.5%
9 994
7.8%
ValueCountFrequency (%)
88 1
 
< 0.1%
79 3
< 0.1%
75 1
 
< 0.1%
72 1
 
< 0.1%
70 2
< 0.1%
68 1
 
< 0.1%
67 1
 
< 0.1%
65 1
 
< 0.1%
64 2
< 0.1%
63 1
 
< 0.1%

percentagenonwesternmigrationbackground
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct91
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.1514154
Minimum0
Maximum96
Zeros1167
Zeros (%)9.2%
Negative0
Negative (%)0.0%
Memory size199.3 KiB
2024-07-05T11:51:51.889327image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q311
95-th percentile35
Maximum96
Range96
Interquartile range (IQR)9

Descriptive statistics

Standard deviation12.109725
Coefficient of variation (CV)1.3232625
Kurtosis8.2700556
Mean9.1514154
Median Absolute Deviation (MAD)4
Skewness2.580155
Sum116708
Variance146.64544
MonotonicityNot monotonic
2024-07-05T11:51:52.555454image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1548
12.1%
2 1393
 
10.9%
3 1220
 
9.6%
0 1167
 
9.2%
4 1040
 
8.2%
5 765
 
6.0%
6 614
 
4.8%
7 469
 
3.7%
8 414
 
3.2%
9 365
 
2.9%
Other values (81) 3758
29.5%
ValueCountFrequency (%)
0 1167
9.2%
1 1548
12.1%
2 1393
10.9%
3 1220
9.6%
4 1040
8.2%
5 765
6.0%
6 614
 
4.8%
7 469
 
3.7%
8 414
 
3.2%
9 365
 
2.9%
ValueCountFrequency (%)
96 1
 
< 0.1%
95 3
< 0.1%
94 2
< 0.1%
93 2
< 0.1%
87 1
 
< 0.1%
86 2
< 0.1%
85 1
 
< 0.1%
84 2
< 0.1%
83 1
 
< 0.1%
82 2
< 0.1%

percentagemen
Real number (ℝ)

Distinct316
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.618294
Minimum10
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size199.3 KiB
2024-07-05T11:51:52.745280image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile46.1
Q148.8
median50.2
Q351.9
95-th percentile56.34
Maximum100
Range90
Interquartile range (IQR)3.1

Descriptive statistics

Standard deviation3.7437041
Coefficient of variation (CV)0.073959507
Kurtosis27.605302
Mean50.618294
Median Absolute Deviation (MAD)1.6
Skewness2.5997335
Sum645535.1
Variance14.01532
MonotonicityNot monotonic
2024-07-05T11:51:52.937971image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 989
 
7.8%
50.6 227
 
1.8%
50.4 222
 
1.7%
49.3 222
 
1.7%
51.4 216
 
1.7%
50.5 209
 
1.6%
50.8 203
 
1.6%
50.3 202
 
1.6%
49.4 200
 
1.6%
49 199
 
1.6%
Other values (306) 9864
77.3%
ValueCountFrequency (%)
10 1
< 0.1%
25.4 1
< 0.1%
30 1
< 0.1%
30.6 1
< 0.1%
30.8 1
< 0.1%
31.2 1
< 0.1%
31.5 1
< 0.1%
32.4 1
< 0.1%
33.3 1
< 0.1%
34.8 1
< 0.1%
ValueCountFrequency (%)
100 5
< 0.1%
96.2 1
 
< 0.1%
94.6 1
 
< 0.1%
91.7 1
 
< 0.1%
90.9 1
 
< 0.1%
90.6 1
 
< 0.1%
90 1
 
< 0.1%
88.4 1
 
< 0.1%
87.5 1
 
< 0.1%
87.1 1
 
< 0.1%

Interactions

2024-07-05T11:51:43.558974image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:22.781017image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:25.398787image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:27.053789image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:28.747115image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:30.652199image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:32.790480image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:34.394408image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:35.994326image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:37.765027image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:39.529747image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:41.626179image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:43.724663image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:22.948688image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:25.531319image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:27.197646image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:28.896931image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:30.779459image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:32.922033image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:34.528549image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:36.144923image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:37.895084image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:39.674564image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:41.759242image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:43.858263image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:23.103750image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:25.665095image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:27.331173image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:29.030177image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:30.916350image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:33.053652image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:34.668214image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:36.277521image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:38.026949image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:39.830395image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:41.892993image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:43.991855image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:23.253742image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:25.797906image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:27.463959image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:29.170493image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:31.046245image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:33.191240image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:34.794273image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:36.411365image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:38.176851image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:39.965116image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:42.032511image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:44.208997image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:23.416099image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:25.947776image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:27.630893image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:29.313638image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:31.237096image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:33.335863image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:34.944911image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:36.577605image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:38.337105image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:40.109829image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:42.192838image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:44.384738image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:23.566261image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:26.098540image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:27.764052image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:29.480736image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:31.395955image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:33.469860image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:35.078321image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:36.710997image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:38.466857image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:40.243047image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:42.387018image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:44.509295image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:23.732225image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:26.231095image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:27.897798image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:29.732484image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:31.548571image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:33.609539image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:35.211372image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:36.844052image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:38.643369image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:40.376621image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:42.592332image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:44.674402image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:24.710632image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:26.365266image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:28.043069image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:29.863702image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:31.679989image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:33.739053image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:35.328307image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:36.994338image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:38.829176image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:40.509965image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:42.748977image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:44.874627image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:24.848619image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:26.514611image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:28.220117image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:30.015547image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:32.189152image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:33.878217image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:35.477998image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:37.161826image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:38.976848image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:41.072455image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:42.892389image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:45.008460image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:24.981981image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:26.647739image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:28.347567image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:30.220099image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:32.381982image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:34.011749image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:35.594437image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:37.293727image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:39.120303image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:41.201239image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:43.059471image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:45.174541image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:25.137198image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:26.781191image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:28.480589image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:30.363360image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:32.521040image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:34.145609image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:35.728020image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:37.465164image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:39.253878image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:41.326432image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:43.236866image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:45.324953image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:25.270429image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:26.915020image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:28.613589image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:30.513142image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:32.652488image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:34.278455image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:35.876144image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:37.595655image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:39.410687image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:41.475365image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:43.392538image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Correlations

2024-07-05T11:51:53.055481image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
percentage0to15yearspercentage15to25yearspercentage25to45yearspercentage45to65yearspercentage65yearsorolderpercentagehouseholdswithchildrenpercentagehouseholdswithoutchildrenpercentagemenpercentagenonwesternmigrationbackgroundpercentageonepersonhouseholdspercentagewesternmigrationbackgroundpopulationdensityperkm2
percentage0to15years1.0000.0320.256-0.212-0.4450.666-0.241-0.0330.092-0.362-0.1910.116
percentage15to25years0.0321.000-0.0310.152-0.4930.336-0.2270.255-0.044-0.169-0.137-0.096
percentage25to45years0.256-0.0311.000-0.585-0.547-0.097-0.596-0.0050.5820.4010.3780.531
percentage45to65years-0.2120.152-0.5851.0000.0320.2670.4770.334-0.501-0.469-0.272-0.516
percentage65yearsorolder-0.445-0.493-0.5470.0321.000-0.4540.490-0.287-0.2280.136-0.052-0.131
percentagehouseholdswithchildren0.6660.336-0.0970.267-0.4541.0000.0130.195-0.246-0.787-0.421-0.234
percentagehouseholdswithoutchildren-0.241-0.227-0.5960.4770.4900.0131.0000.134-0.565-0.535-0.365-0.481
percentagemen-0.0330.255-0.0050.334-0.2870.1950.1341.000-0.284-0.256-0.159-0.409
percentagenonwesternmigrationbackground0.092-0.0440.582-0.501-0.228-0.246-0.565-0.2841.0000.5180.5800.726
percentageonepersonhouseholds-0.362-0.1690.401-0.4690.136-0.787-0.535-0.2560.5181.0000.5410.470
percentagewesternmigrationbackground-0.191-0.1370.378-0.272-0.052-0.421-0.365-0.1590.5800.5411.0000.469
populationdensityperkm20.116-0.0960.531-0.516-0.131-0.234-0.481-0.4090.7260.4700.4691.000

Missing values

2024-07-05T11:51:45.536093image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-07-05T11:51:45.874113image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

neighborhoodcodeneighborhoodnamepopulationdensityperkm2percentage0to15yearspercentage15to25yearspercentage25to45yearspercentage45to65yearspercentage65yearsorolderpercentageonepersonhouseholdspercentagehouseholdswithoutchildrenpercentagehouseholdswithchildrenpercentagewesternmigrationbackgroundpercentagenonwesternmigrationbackgroundpercentagemen
0BU00140000Binnenstad-Noord12085.02.049.033.010.06.082.015.03.020.011.052.1
1BU06640551De Schenge16.022.08.023.031.017.015.038.048.08.02.052.4
2BU18960310Streukel-Holten-Genne30.019.013.016.030.022.016.038.046.04.01.051.4
3BU03922001Zuid-Schalkwijkerweg297.08.012.011.048.021.028.041.030.08.05.054.3
4BU18760403Drempt2167.013.012.020.031.024.027.040.033.07.04.050.5
5BU08551704Schildersbuurt Zuid12135.07.026.035.023.09.065.020.015.027.011.055.1
6BU02431207Harderhout II7335.030.010.033.021.07.016.022.062.05.010.048.6
7BU01601903Verspreide huizen Lutten-Oost401.013.011.024.033.020.023.033.044.05.03.049.0
9BU04792130Het Eiland9972.013.014.013.033.027.027.034.039.012.013.049.8
10BU08650100Voorburg en omgeving994.010.09.026.026.030.036.032.031.09.08.050.6
neighborhoodcodeneighborhoodnamepopulationdensityperkm2percentage0to15yearspercentage15to25yearspercentage25to45yearspercentage45to65yearspercentage65yearsorolderpercentageonepersonhouseholdspercentagehouseholdswithoutchildrenpercentagehouseholdswithchildrenpercentagewesternmigrationbackgroundpercentagenonwesternmigrationbackgroundpercentagemen
14307BU03750303De Naald11000.016.07.029.026.023.034.037.029.012.016.049.4
14308BU03610205Oudorperpolder-Midden5136.017.09.026.023.025.023.040.037.012.013.049.5
14309BU07790001Hooipolder en De Hoeven4164.017.011.024.030.018.023.036.041.07.05.050.4
14310BU04530303Zuidzijdebuurt96.07.010.019.042.023.061.024.015.026.08.060.7
14311BU19110301Wieringerwaard (woonkern)2667.017.010.025.029.019.031.031.038.011.03.052.2
14312BU04310000Kerkbuurt2011.014.012.020.030.024.032.031.038.07.09.048.4
14313BU17110102In de Mehre3624.015.013.015.032.025.019.037.043.011.01.049.6
14314BU03631200Houthavens West9848.023.05.041.024.08.037.027.035.023.016.050.7
14315BU01935050Oldenelerlanden-West3904.018.011.020.036.016.019.039.042.05.06.050.4
14316BU08200009Verspreide huizen ten zuidoosten van Nuenen14.09.05.019.027.041.034.034.031.08.02.046.2